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Update train.ipynb
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@syx777
syx777 committed Jun 8, 2024
1 parent ad03d5c commit c104485
Showing 1 changed file with 159 additions and 24 deletions.
183 changes: 159 additions & 24 deletions scripts/train.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
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" 'learning_rate': 0.002,\n",
" 'batch_size': 2048,\n",
" 'num_epochs': 10,\n",
" 'num_dim': 1, # for IRT or MIRT\n",
" 'num_dim': 10, # for IRT or MIRT\n",
" 'device': 'cpu',\n",
" # for NeuralCD\n",
" 'prednet_len1': 128,\n",
" 'prednet_len2': 64,\n",
" 'betas': (0.9, 0.999),\n",
" 'betas':(0.9, 0.999),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# read datasets\n",
"train_triplets = pd.read_csv(f'../data/{dataset}/train_triples.csv', encoding='utf-8').to_records(index=False)\n",
"concept_map = json.load(open(f'../data/{dataset}/concept_map.json', 'r'))\n",
"train_triplets = pd.read_csv(f'./dataset/train_triples.csv', encoding='utf-8').to_records(index=False)\n",
"concept_map = json.load(open(f'./dataset/concept_map.json', 'r'))\n",
"concept_map = {int(k):v for k,v in concept_map.items()}\n",
"metadata = json.load(open(f'../data/{dataset}/metadata.json', 'r'))"
"metadata = json.load(open(f'./dataset/metadata.json', 'r'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'betas'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27436\\1368016865.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCAT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIRTModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# train model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog_step\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\workplace\\tmp\\EduCAT\\CAT\\model\\IRT.py\u001b[0m in \u001b[0;36minit_model\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[0mpolicy_lr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.0005\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIRT\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_students\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'num_dim'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolicy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStraightThrough\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpolicy_lr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 57\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_q\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\workplace\\tmp\\EduCAT\\CAT\\model\\utils.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, state_dim, action_dim, lr, config)\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0mdevice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'device'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbetas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'betas'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 37\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolicy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mActor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_dim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction_dim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m self.optimizer = torch.optim.Adam(\n",
"\u001b[1;31mKeyError\u001b[0m: 'betas'"
"name": "stdout",
"output_type": "stream",
"text": [
"[INFO 2024-06-08 17:47:52,872] train on cpu\n",
"[INFO 2024-06-08 17:47:52,872] train on cpu\n",
"[INFO 2024-06-08 17:47:52,933] Epoch [1] Batch [0]: loss=inf\n",
"[INFO 2024-06-08 17:47:52,933] Epoch [1] Batch [0]: loss=inf\n",
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"[INFO 2024-06-08 17:48:23,514] Epoch [8] Batch [60]: loss=0.63759\n",
"[INFO 2024-06-08 17:48:23,750] Epoch [9] Batch [0]: loss=inf\n",
"[INFO 2024-06-08 17:48:23,750] Epoch [9] Batch [0]: loss=inf\n",
"[INFO 2024-06-08 17:48:24,344] Epoch [9] Batch [10]: loss=0.68184\n",
"[INFO 2024-06-08 17:48:24,344] Epoch [9] Batch [10]: loss=0.68184\n",
"[INFO 2024-06-08 17:48:24,940] Epoch [9] Batch [20]: loss=0.65004\n",
"[INFO 2024-06-08 17:48:24,940] Epoch [9] Batch [20]: loss=0.65004\n",
"[INFO 2024-06-08 17:48:25,525] Epoch [9] Batch [30]: loss=0.63936\n",
"[INFO 2024-06-08 17:48:25,525] Epoch [9] Batch [30]: loss=0.63936\n",
"[INFO 2024-06-08 17:48:26,229] Epoch [9] Batch [40]: loss=0.63363\n",
"[INFO 2024-06-08 17:48:26,229] Epoch [9] Batch [40]: loss=0.63363\n",
"[INFO 2024-06-08 17:48:26,806] Epoch [9] Batch [50]: loss=0.62970\n",
"[INFO 2024-06-08 17:48:26,806] Epoch [9] Batch [50]: loss=0.62970\n",
"[INFO 2024-06-08 17:48:27,378] Epoch [9] Batch [60]: loss=0.62724\n",
"[INFO 2024-06-08 17:48:27,378] Epoch [9] Batch [60]: loss=0.62724\n",
"[INFO 2024-06-08 17:48:27,612] Epoch [10] Batch [0]: loss=inf\n",
"[INFO 2024-06-08 17:48:27,612] Epoch [10] Batch [0]: loss=inf\n",
"[INFO 2024-06-08 17:48:28,173] Epoch [10] Batch [10]: loss=0.67117\n",
"[INFO 2024-06-08 17:48:28,173] Epoch [10] Batch [10]: loss=0.67117\n",
"[INFO 2024-06-08 17:48:28,768] Epoch [10] Batch [20]: loss=0.63896\n",
"[INFO 2024-06-08 17:48:28,768] Epoch [10] Batch [20]: loss=0.63896\n",
"[INFO 2024-06-08 17:48:29,350] Epoch [10] Batch [30]: loss=0.62850\n",
"[INFO 2024-06-08 17:48:29,350] Epoch [10] Batch [30]: loss=0.62850\n",
"[INFO 2024-06-08 17:48:30,064] Epoch [10] Batch [40]: loss=0.62304\n",
"[INFO 2024-06-08 17:48:30,064] Epoch [10] Batch [40]: loss=0.62304\n",
"[INFO 2024-06-08 17:48:30,665] Epoch [10] Batch [50]: loss=0.61968\n",
"[INFO 2024-06-08 17:48:30,665] Epoch [10] Batch [50]: loss=0.61968\n",
"[INFO 2024-06-08 17:48:31,261] Epoch [10] Batch [60]: loss=0.61717\n",
"[INFO 2024-06-08 17:48:31,261] Epoch [10] Batch [60]: loss=0.61717\n"
]
}
],
Expand All @@ -118,12 +253,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# save model\n",
"model.adaptest_save('../ckpt/irt.pt')"
"model.adaptest_save('../ckpt/mirt.pt')"
]
}
],
Expand Down

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